Advertisement

Human Posture Analysis Under Partial Self-occlusion

  • Ruixuan Wang
  • Wee Kheng Leow
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4141)

Abstract

Accurate human posture estimation from single or multiple images is essential in many applications. Two main causes of difficulty to solve the estimation problem are large number of degrees of freedom and self-occlusion. Tree-structured graphical models with efficient inference algorithms have been used to solve the problem in a lower dimensional state space. However, such models are not accurate enough to formulate the problem because it assumes that the image of each body part can be independently observed. As a result, it is difficult to handle partial self-occlusion. This paper presents a more accurate graphical model which can implicitly model the possible self-occlusion between body parts. More important, an efficiently approximate inference algorithm is provided to estimate human posture in a low dimensional state space. It can deal with partial self-occlusion in posture estimation and human tracking, which has been shown by the experimental results on real data.

Keywords

Body Part Posture Estimation IEEE Conf Inference Algorithm Human Posture 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agarwal, A., Triggs, B.: 3d human pose from silhouettes by relevance vector regression. In: Proc. IEEE Conf. on CVPR, pp. 882–888 (2004)Google Scholar
  2. 2.
    Athitsos, V., Alon, J., Sclaroff, S., Kollios, G.: Boostmap: A method for efficient approximate similarity rankings. In: Proc. IEEE Conf. on CVPR, pp. 268–275 (2004)Google Scholar
  3. 3.
    Cham, T., Rehg, J.: A multiple hypothesis approach to figure tracking. In: Proc. IEEE Conf. on CVPR, pp. 239–245 (1999)Google Scholar
  4. 4.
    Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Machine Intell. 24, 603–619 (2002)CrossRefGoogle Scholar
  5. 5.
    Deutscher, J., Blake, A., Reid, I.: Articulated body motion capture by annealed particle filtering. In: Proc. IEEE Conf. on CVPR, pp. 126–133 (2000)Google Scholar
  6. 6.
    Elgammal, A., Lee, C.: Inferring 3d body pose from silhouettes using activity manifold learning. In: Proc. IEEE Conf. on CVPR, pp. 681–688 (2004)Google Scholar
  7. 7.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Pictorial structures for object recognition. Int. Journal of Computer Vision 61(1), 55–79 (2005)CrossRefGoogle Scholar
  8. 8.
    Gavrila, D.M.: The visual analysis of human movement: A survey. Computer Vision and Image Understanding: CVIU 73(1), 82–98 (1999)CrossRefMATHGoogle Scholar
  9. 9.
    Hua, G., Yang, M.H., Wu, Y.: Learning to estimate human pose with data driven belief propagation. In: Proc. IEEE Conf. on CVPR, pp. 747–754 (2005)Google Scholar
  10. 10.
    Ioffe, S., Forsyth, D.: Finding people by sampling. In: Proc. IEEE Conf. on ICCV, pp. 1092–1097 (1999)Google Scholar
  11. 11.
    Isard, M.: Pampas: Real-valued graphical models for computer vision. In: Proc. IEEE Conf. on CVPR, pp. 613–620 (2003)Google Scholar
  12. 12.
    Isard, M., Blake, A.: Contour tracking by stochastic propagation of conditional density. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1064, pp. 343–356. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  13. 13.
    Lan, X., Huttenlocher, D.P.: Beyond trees: common-factor models for 2D human pose recovery. In: Proc. IEEE Conf. on ICCV, pp. 470–477 (2005)Google Scholar
  14. 14.
    Lee, M.W., Cohen, I.: Proposal maps driven mcmc for estimating human body pose in static images. In: Proc. IEEE Conf. on CVPR, pp. 334–341 (2004)Google Scholar
  15. 15.
    Mori, G.: Guiding model search using segmentation. In: Proc. IEEE Conf. on ICCV (2005)Google Scholar
  16. 16.
    Mori, G., Malik, J.: Estimating human body configurations using shape context matching. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 666–680. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  17. 17.
    Urtasun, A.H.R., Fleet, D.J., Fua, P.: Priors for people tracking from small training sets. In: IEEE Int. Conf. ICCV (2005)Google Scholar
  18. 18.
    Ramanan, D., Forsyth, D.A., Zisserman, A.: Strike a pose: Tracking people by finding stylized poses. In: Proc. IEEE Conf. on CVPR, pp. 271–278 (2005)Google Scholar
  19. 19.
    Rosales, R., Athitsos, V., Sclaroff, S.: 3D hand pose reconstruction using specialized mappings. In: Proc. IEEE Conf. on ICCV, pp. 378–385 (2001)Google Scholar
  20. 20.
    Sigal, L., Bhatia, S., Roth, S., Black, M., Isard, M.: Tracking loose-limbed people. In: Proc. IEEE Conf. on CVPR, pp. 421–428 (2004)Google Scholar
  21. 21.
    Sminchisescu, C., Kanaujia, A., Li, Z., Metaxas, D.: Discriminative density propagation for 3D human motion estimation. In: Proc. IEEE Conf. on CVPR, pp. 390–397 (2005)Google Scholar
  22. 22.
    Sminchisescu, C., Triggs, B.: Kinematic jump processes for monocular 3D human tracking. In: Proc. IEEE Conf. on CVPR, pp. 69–76 (2003)Google Scholar
  23. 23.
    Sminchisescu, C., Triggs, B.: Building roadmaps of minima and transitions in visual models. Int. Journal of Computer Vision 61(1), 81–101 (2005)CrossRefGoogle Scholar
  24. 24.
    Sudderth, E., Ihler, A., Freeman, W., Willsky, A.: Nonparametric belief propagation. In: Proc. IEEE Conf. on CVPR, pp. 605–612 (2003)Google Scholar
  25. 25.
    Sudderth, E., Mandel, M., Freeman, W., Willsky, A.: Distributed occlusion reasoning for tracking with nonparametric belief propagation. In: NIPS (2004)Google Scholar
  26. 26.
    Sudderth, E., Mandel, M., Freeman, W., Willsky, A.: Visual hand tracking using nonparametric belief propagation. In: IEEE CVPR Workshop on Generative Model based Vision (2004)Google Scholar
  27. 27.
    Wang, R., Leow, W.K.: Human body posture refinement by nonparametric belief propagation. In: IEEE Conf. on ICIP (2005)Google Scholar
  28. 28.
    Yedidia, J.S., Freeman, W.T., Weiss, Y.: Constructing free energy approximations and generalized belief propagation algorithms. Technical report, MERL (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ruixuan Wang
    • 1
  • Wee Kheng Leow
    • 1
  1. 1.School of ComputingNational University of SingaporeSingaporeSingapore

Personalised recommendations